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Theory_of_Mind_128_StableLM

PEFT
stablelm_epoch
Generated from Trainer
custom_code
4-bit precision
bitsandbytes
Model card Files Community
Theory_of_Mind_128_StableLM
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---
license: cc-by-sa-4.0
library_name: peft
tags:
- generated_from_trainer
base_model: stabilityai/stablelm-3b-4e1t
model-index:
- name: qlora-out-2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.3.0`
```yaml
base_model: stabilityai/stablelm-3b-4e1t
base_model_config: stabilityai/stablelm-3b-4e1t
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: GPTNeoXTokenizerFast

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: theory_of_mind_airoboros_fixed.json
    type: alpaca

dataset_prepared_path:
val_set_size: 0.005
output_dir: ./qlora-out-2

adapter: qlora

wandb_project: theoryofmind
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

sequence_len: 1024
sample_packing: false
pad_to_sequence_len: true
save_safetensors: false

lora_r: 128
lora_alpha: 256
lora_dropout: 0.05
lora_target_linear: false
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head
lora_target_modules:
  - q_proj
  - v_proj

gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 5
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.00005

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 1
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|endoftext|>"
  eos_token: "<|im_end|>"
  unk_token: "<|endoftext|>"
tokens:
  - "<|im_start|>"
  - "<|im_end|>"

```

</details><br>

# qlora-out-2

This model is a fine-tuned version of [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9864

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.928         | 0.05  | 1    | 1.7816          |
| 1.2231        | 1.0   | 22   | 1.1896          |
| 0.8273        | 2.0   | 44   | 1.0456          |
| 0.517         | 3.0   | 66   | 0.9905          |
| 1.0244        | 4.0   | 88   | 0.9915          |
| 0.6749        | 5.0   | 110  | 0.9864          |


### Framework versions

- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0